CN113516299A - Ship fault state prediction method and system - Google Patents

Ship fault state prediction method and system Download PDF

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CN113516299A
CN113516299A CN202110601739.8A CN202110601739A CN113516299A CN 113516299 A CN113516299 A CN 113516299A CN 202110601739 A CN202110601739 A CN 202110601739A CN 113516299 A CN113516299 A CN 113516299A
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邱伯华
张羽
魏慕恒
孙文秋实
关文渊
张瑞
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Zhendui Industrial Intelligent Technology Co ltd
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Abstract

The invention relates to a ship fault state prediction method and a system, and solves the problem that the ship fault state prediction method is low in accuracy. The method comprises the steps of collecting M-dimensional measurement data in a historical time period, wherein the M-dimensional measurement data comprises measurement data in a normal working state and measurement data in a maintenance state; based on the M-dimensional measurement data, calculating to obtain a transfer matrix between a normal working state and a maintenance state and a drift coefficient and a diffusion coefficient of each degradation process, wherein the one-dimensional measurement data corresponds to one degradation process; setting a failure threshold value of each degradation process, and obtaining cumulative probability density distribution of failure time of each degradation process by combining a Monte Carlo method based on a transfer matrix, a drift coefficient and a diffusion coefficient of each degradation process; and obtaining the joint probability density distribution of the ship fault state by combining the normal Copula function based on the cumulative probability density distribution of the failure time of each degradation process. The accuracy of the ship fault state prediction is improved.

Description

Ship fault state prediction method and system
Technical Field
The invention relates to the technical field of ships, in particular to a ship fault state prediction method and system.
Background
Existing fault state prediction techniques mainly include two major categories: the mechanism-based method and the data-based method have the advantages that the former method has better interpretability, but the actual system usually has extremely complex mechanism and small application range, and the latter method is the mainstream method at present.
The traditional method considers that the degradation process of the ship is uniform, namely, only a section of uniform degradation process is carried out from the ship delivery to the ship scrapping, and the method is over-ideal, so that the prediction result of the fault state of the ship is inaccurate.
Based on the analysis, a method and a system for predicting the fault state of the ship are urgently needed, and the fault state of the ship can be predicted more accurately.
Disclosure of Invention
In view of the foregoing analysis, embodiments of the present invention provide a method and a system for predicting a ship fault state, so as to solve the problem that the existing ship fault state prediction method is not high in accuracy.
In one aspect, an embodiment of the present invention provides a method for predicting a ship fault state, including:
collecting M-dimensional measurement data in a historical period, wherein the M-dimensional measurement data comprises measurement data in a normal working state and measurement data in a maintenance state;
calculating a transfer matrix between a normal working state and a maintenance state and a drift coefficient and a diffusion coefficient of each degradation process based on the M-dimensional measurement data, wherein the one-dimensional measurement data corresponds to one degradation process;
setting a failure threshold value of each degradation process, and obtaining cumulative probability density distribution of failure time of each degradation process by combining a Monte Carlo method based on the transfer matrix, the drift coefficient and the diffusion coefficient of each degradation process;
and obtaining the joint probability density distribution of the ship fault state by combining the normal Copula function based on the cumulative probability density distribution of the failure time of each degradation process.
Further, based on the M-dimensional measurement data, a transition matrix between a normal operating state and a maintenance state is calculated, including:
obtaining the times of switching the normal working state to the maintenance state, the times of switching the maintenance state to the normal working state, the total duration of the normal working state before the last state switching in the history period and the total duration of the maintenance state based on the M-dimensional measurement data;
and calculating to obtain a transition matrix between the normal working state and the maintenance state by combining a maximum likelihood estimation method according to the times of converting the normal working state to the maintenance state, the times of converting the maintenance state to the normal working state, the total duration of the normal working state before the last state switching in the history period and the total duration of the maintenance state.
Further, calculating a transition matrix between the normal working state and the maintenance state, including:
calculating the state transition rate lambda of the normal working state to the maintenance state according to the following formula01And the transition rate lambda of the maintenance state to the normal operating state10：
Figure BDA0003092884170000021
Wherein n is01Representing the times of the normal working state to the maintenance state in the historical period; n is10Representing the times of the maintenance state to be converted into the normal working state in the historical time period; t is0Representing the total duration of the normal working state before the last state switching in the historical period; t is1Representing the total duration of the maintenance state before the last occurrence of the state switch in the history period;
calculating the probability P of state transition from normal operation state to maintenance state according to the following formula01And the probability P of state transition of maintenance state to normal working state10：
Figure BDA0003092884170000031
Wherein t represents the sampling period of the measurement data in the historical period;
according to the state transition probability P of the normal working state to the maintenance state01And the probability P of state transition of maintenance state to normal working state10Obtaining a transition matrix between the normal working state and the maintenance state
Figure BDA0003092884170000032
Further, calculating a drift coefficient and a diffusion coefficient of each degradation process based on the M-dimensional measurement data, including:
calculating the drift coefficient of the m degradation process under the normal working state according to the following formula
Figure BDA0003092884170000033
And the drift coefficient in the mth degradation process maintenance state
Figure BDA0003092884170000034
Figure BDA0003092884170000035
Wherein phi isj0 denotes the normal operating state, phij1 represents the maintenance state, k represents the number of samples of the measurement data corresponding to the mth degradation process,
Figure BDA0003092884170000036
represents the j +1 th sample value of the m-th degeneration process,
Figure BDA0003092884170000037
j sample value, t, representing the m degradation processj+1Presentation collection
Figure BDA0003092884170000038
Time of (t)jPresentation collection
Figure BDA0003092884170000039
The time of day;
the diffusion coefficient σ of the mth degradation process is calculated according to the following formula(m)；
Figure BDA00030928841700000310
Wherein the content of the first and second substances,
Figure BDA00030928841700000311
to represent
Figure BDA00030928841700000312
The corresponding drift coefficient under the ship state is the drift coefficient under the normal working state
Figure BDA00030928841700000313
Or drift coefficient in maintenance state
Figure BDA00030928841700000314
Further, setting a failure threshold value of each degradation process, and obtaining cumulative probability density distribution of failure time of each degradation process by combining a monte carlo method based on the transfer matrix, the drift coefficient and the diffusion coefficient of each degradation process, wherein the failure threshold value comprises the following steps:
based on the current time t of the m-th degeneration process according to the following formulakObtaining a predicted value of the mth degradation process at a future moment by using the corresponding sampling value, the transfer matrix, and the drift coefficient and the diffusion coefficient of the mth degradation process:
Figure BDA0003092884170000041
wherein the content of the first and second substances,
Figure BDA0003092884170000042
representing the current time t of the m-th degeneration processkDrift coefficient, phi, corresponding to the ship statekIndicates the current time tkThe corresponding state of the vessel is the state of the vessel,
Figure BDA0003092884170000043
representing the mth predicted time t of the degradation processk+1The corresponding predicted value is set to be a predicted value,
Figure BDA0003092884170000044
indicates the current time tkThe corresponding value of the sampled value is,
Figure BDA0003092884170000045
to satisfy
Figure BDA0003092884170000046
The random number of (2);
Figure BDA0003092884170000047
representing the mth predicted time t of the degradation processk+s-1Drift coefficient of corresponding ship state, from phik+s-1Is determined by the state of phik+s-1Representing future predicted time tk+s-1The corresponding ship state is determined by the state of the previous prediction time and the transition matrix,
Figure BDA0003092884170000048
representing the mth predicted time t of the degradation processk+sThe corresponding predicted value is set to be a predicted value,
Figure BDA0003092884170000049
representing future predicted time tk+s-1The corresponding predicted value, s is more than or equal to 2,
Figure BDA00030928841700000410
to satisfy
Figure BDA00030928841700000411
A distributed random number;
up to
Figure BDA00030928841700000412
Reaching the failure threshold of the mth degradation process for the first time, wherein the time interval between the moment reaching the failure threshold and the current moment is the failure time;
and obtaining a plurality of failure times of the mth degradation process based on a Monte Carlo method, and further obtaining cumulative probability density distribution of the plurality of failure times of each degradation process.
Further, the obtaining of the joint probability density distribution of the ship fault state based on the cumulative probability density distribution of the failure time of each degradation process in combination with the normal Copula function includes:
obtaining a joint probability density distribution of the ship fault state according to the following formula:
F(l)＝C(F(1)(l(1)),F(2)(l(2)),...,F(M)(l(M))；ρ)
＝Φρ(Φ-1(F(1)(l(1)))，Φ-1(F(2)(l(2)))，...Φ-1(F(M)(l(M))))
wherein F (l) represents that the current time is tkJoint probability density distribution of time, ship fault states, F(1)(l(1)) Indicating that the current time is tkWhen the temperature of the water is higher than the set temperature,cumulative probability density distribution of time to failure of the first degeneration process, F(2)(l(2)) Indicating that the current time is tkCumulative probability density distribution of time to failure of the second degeneration process, F(M)(l(M)) Indicating that the current time is tkCumulative probability density distribution of time to failure, phi, of the Mth degeneration processρA joint cumulative distribution function of M-dimensional normal distribution with mean as zero vector and covariance matrix as rho, phi-1An inverse cumulative distribution function representing a standard normal distribution.
Further, the method further comprises calculating ρ by the following formula:
Figure BDA0003092884170000051
wherein, Fi(l) Indicating that the current time is tiAnd then, i is more than or equal to 1 and less than or equal to k in the calculated joint probability density distribution of the ship fault state.
In another aspect, an embodiment of the present invention provides a system for predicting a ship fault state, including:
the data acquisition module is used for acquiring M-dimensional measurement data in a historical time period, wherein the M-dimensional measurement data comprises measurement data in a normal working state and measurement data in a maintenance state;
the transfer matrix calculation module is used for calculating to obtain a transfer matrix between a normal working state and a maintenance state based on the M-dimensional measurement data;
the drift coefficient and diffusion coefficient calculation module is used for calculating the drift coefficient and diffusion coefficient of each degradation process based on the M-dimensional measurement data, and the one-dimensional measurement data corresponds to one degradation process;
the cumulative probability density distribution acquisition module is used for setting a failure threshold value of each degradation process, and obtaining the cumulative probability density distribution of failure time of each degradation process based on the transfer matrix, the drift coefficient and the diffusion coefficient of each degradation process and in combination with a Monte Carlo method;
and the joint probability density distribution acquisition module is used for obtaining the joint probability density distribution of the ship fault state by combining the normal Copula function based on the cumulative probability density distribution of the failure time of each degradation process.
Further, the transition matrix calculation module includes:
a transfer rate calculation module for calculating the state transfer rate lambda of the normal working state to the maintenance state according to the following formula01And the transition rate lambda of the maintenance state to the normal operating state10：
Figure BDA0003092884170000061
Wherein n is01Representing the times of the normal working state to the maintenance state in the historical period; n is10Representing the times of the maintenance state to be converted into the normal working state in the historical time period; t is0Representing the total duration of the normal working state before the last state switching in the historical period; t is1Representing the total duration of the maintenance state before the last occurrence of the state switch in the history period;
a transition probability calculation module for calculating the state transition probability P of the normal working state to the maintenance state according to the following formula01And the probability P of state transition of maintenance state to normal working state10：
Figure BDA0003092884170000062
Wherein t represents the sampling period of the measurement data in the historical period;
a transition matrix generation module for converting the normal working state into a state transition probability P of the maintenance state01And the probability P of state transition of maintenance state to normal working state10Obtaining a transition matrix between the normal working state and the maintenance state
Figure BDA0003092884170000063
Further, the drift coefficient and diffusion coefficient calculation module includes:
a drift coefficient calculation module for calculating the drift coefficient of the m-th degradation process under normal working condition according to the following formula
Figure BDA0003092884170000071
And the drift coefficient in the mth degradation process maintenance state
Figure BDA0003092884170000072
Figure BDA0003092884170000073
Wherein phi isj0 denotes the normal operating state, phij1 represents the maintenance state, k represents the number of samples of the measurement data corresponding to the mth degradation process,
Figure BDA0003092884170000074
represents the j +1 th sample value of the m-th degeneration process,
Figure BDA0003092884170000075
j sample value, t, representing the m degradation processj+1Presentation collection
Figure BDA0003092884170000076
Time of (t)jPresentation collection
Figure BDA0003092884170000077
The time of day;
a diffusion coefficient calculation module for calculating the diffusion coefficient sigma of the mth degradation process according to the following formula(m)；
Figure BDA0003092884170000078
Wherein the content of the first and second substances,
Figure BDA0003092884170000079
to represent
Figure BDA00030928841700000710
The corresponding drift coefficient under the ship state is the drift coefficient under the normal working state
Figure BDA00030928841700000711
Or drift coefficient in maintenance state
Figure BDA00030928841700000712
Compared with the prior art, the invention can at least realize the following beneficial effects:
the technical scheme includes that M-dimensional measurement data in a historical time period are collected, each piece of the M-dimensional measurement data comprises normal working state measurement data and maintenance state measurement data, a transition matrix between a normal working state and a maintenance state and a drift coefficient and a diffusion coefficient of each degradation process are obtained through calculation based on the collected M-dimensional measurement data, cumulative probability density distribution of failure time of each degradation process is obtained through combination of a Monte Carlo method, and then a joint probability density distribution of a ship fault state is obtained through combination of a normal copula function; the influence of maintenance activities on the ship fault state is introduced into the prediction on the ship fault state, and the accuracy of the ship fault state prediction is improved by considering the influence of the normal working state and the maintenance state on the ship fault state.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a schematic flow chart of a ship fault state prediction method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a ship fault state prediction system according to an embodiment of the present application.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
The invention discloses a ship fault state prediction method, a specific flow chart is shown in figure 1, and the method comprises the following steps:
step S10: collecting M-dimensional measurement data in a historical period, wherein the M-dimensional measurement data comprises measurement data in a normal working state and measurement data in a maintenance state;
step S20: calculating a transfer matrix between a normal working state and a maintenance state and a drift coefficient and a diffusion coefficient of each degradation process based on the M-dimensional measurement data, wherein the one-dimensional measurement data corresponds to one degradation process;
step S30: setting a failure threshold value of each degradation process, and obtaining cumulative probability density distribution of failure time of each degradation process by combining a Monte Carlo method based on the transfer matrix, the drift coefficient and the diffusion coefficient of each degradation process;
step S40: and obtaining the joint probability density distribution of the ship fault state by combining the normal Copula function based on the cumulative probability density distribution of the failure time of each degradation process.
Compared with the prior art, according to the ship fault state prediction method provided by the embodiment, the technical scheme is that the ship fault state combined probability density distribution is obtained by collecting historical time interval M-dimensional measurement data, wherein each dimension of the M-dimensional measurement data comprises normal working state measurement data and maintenance state measurement data, calculating to obtain a transition matrix between a normal working state and a maintenance state and a drift coefficient and a diffusion coefficient of each degradation process based on the collected M-dimensional measurement data, combining a Monte Carlo method to obtain the cumulative probability density distribution of each degradation process failure time, and further combining a normal copula function to obtain the combined probability density distribution of the ship fault state; the influence of maintenance activities on the ship fault state is introduced into the prediction on the ship fault state, and the accuracy of the ship fault state prediction is improved by considering the influence of the normal working state and the maintenance state on the ship fault state.
Specifically, the M-dimensional measurement data in step S10 includes multidimensional measurement data such as generator power, host rotation speed, loss rate, cooling water temperature, lubricant pressure, and lubricant temperature, and can be selected according to actual requirements, and the more the selected parameter dimensions are, the more accurate the corresponding prediction result is; the method comprises the steps of collecting M-dimensional measurement data by utilizing sensors of a ship, recording one-dimensional measurement data by each sensor, and forming measurement data of the dimension of the power of a generator by continuously sampling, wherein the measurement data are used for recording power data of the generator in historical time periods by power sensors of the generator. Further, each measurement data in each dimension is provided with a state label, and the state label refers to a maintenance state or a normal working state and is used for representing the ship state when the measurement data is acquired, so that each dimension of measurement data comprises the measurement data in the normal working state and the measurement data in the maintenance state. Further, the history period in step S10 is a time interval from a certain past time to the current time, that is, the current time is preceded by the history data that needs to be collected, the current time is followed by the future data that needs to be predicted, and the data corresponding to the current time is the sampling value.
In a specific embodiment, step S20 includes:
step S21: calculating to obtain a transfer matrix between a normal working state and a maintenance state based on the M-dimensional measurement data;
step S22: and calculating the drift coefficient and the diffusion coefficient of each degradation process based on the M-dimensional measurement data.
In a specific embodiment, step S21 includes:
step S211: obtaining the times of switching the normal working state to the maintenance state, the times of switching the maintenance state to the normal working state, the total duration of the normal working state before the last state switching in the history period and the total duration of the maintenance state based on the M-dimensional measurement data;
specifically, each measurement data in each dimension has a state label, and the state label is a maintenance state or a normal working state and is used for representing the state of the ship when the measurement data is acquired, so that after the M-dimensional measurement data in the history period is acquired, the times of converting the normal working state to the maintenance state, the times of converting the maintenance state to the normal working state, the total duration of the normal working state before the last state switching in the history period, and the total duration of the maintenance state can be obtained through statistics.
Step S212: and calculating to obtain a transition matrix between the normal working state and the maintenance state by combining a maximum likelihood estimation method according to the times of converting the normal working state to the maintenance state, the times of converting the maintenance state to the normal working state, the total duration of the normal working state before the last state switching in the history period and the total duration of the maintenance state.
In a specific embodiment, step S212 includes:
step S2121: calculating the state transition rate lambda of the normal working state to the maintenance state according to the formula (1)01And the transition rate lambda of the maintenance state to the normal operating state10：
Figure BDA0003092884170000101
Wherein n is01Representing the times of the normal working state to the maintenance state in the historical period; n is10Representing dimensions within a historical periodThe number of times of switching the protection state to the normal working state; t is0Representing the total duration of the normal working state before the last state switching in the historical period; t is1Indicating the total length of time in the history period that the device was in maintenance before the last occurrence of a state switch.
Specifically, the last occurrence of the state switching may be the transition from the normal operating state to the maintenance state or the transition from the maintenance state to the normal operating state.
Step S2122: according to the formula (2), calculating the state transition probability P of the normal working state to the maintenance state01And the probability P of state transition of maintenance state to normal working state10：
Figure BDA0003092884170000111
Where t represents the sampling period of the historical period measurement data.
Step S2123: according to the state transition probability P of the normal working state to the maintenance state01And the probability P of state transition of maintenance state to normal working state10Obtaining a transition matrix between the normal working state and the maintenance state
Figure BDA0003092884170000112
Specifically, 1-P01Representing the probability of maintaining a normal working state, 1-P10Representing the probability of maintaining the maintenance state.
In a specific embodiment, step S22 includes:
step S221: calculating the drift coefficient of the m degradation process under the normal working state according to the formula (3.1) and the formula (3.2)
Figure BDA0003092884170000113
And the drift coefficient in the mth degradation process maintenance state
Figure BDA0003092884170000114
Figure BDA0003092884170000115
Figure BDA0003092884170000121
Wherein phi isj0 denotes the normal operating state, phij1 represents the maintenance state, k represents the number of samples of the measurement data corresponding to the mth degradation process,
Figure BDA0003092884170000122
represents the j +1 th sample value of the m-th degeneration process,
Figure BDA0003092884170000123
j sample value, t, representing the m degradation processj+1Presentation collection
Figure BDA0003092884170000124
Time of (t)jPresentation collection
Figure BDA0003092884170000125
The time of day.
Step S222: calculating the diffusion coefficient sigma of the mth degradation process according to equation (4)(m)；
Figure BDA0003092884170000126
Wherein the content of the first and second substances,
Figure BDA0003092884170000127
to represent
Figure BDA0003092884170000128
The corresponding drift coefficient under the ship state is the drift coefficient under the normal working state
Figure BDA0003092884170000129
Or drift coefficient in maintenance state
Figure BDA00030928841700001210
Specifically, the measurement data in the mth degradation process history period includes the sampling value a1、a2、a3、a4、a5、a6For the purpose of illustration, a1To a6Respectively corresponding to the sampling time t1To t6，a1、a2And a5Corresponding to the normal operating state (i.e., 0 state), a3、a4And a6Corresponding to the maintenance state (i.e., 1 state), the drift coefficient under the normal operating state of the mth degradation process
Figure BDA00030928841700001211
Satisfy the requirement of
Figure BDA00030928841700001212
Drift coefficient under maintenance condition of mth degradation process
Figure BDA00030928841700001213
Satisfy the requirement of
Figure BDA00030928841700001214
Diffusion coefficient sigma(m)Satisfy the requirement of
Figure BDA00030928841700001215
Specifically, each dimension of measurement data corresponds to a degradation process, the mth degradation process is a degradation process corresponding to the mth dimension of measurement data in the M dimension of measurement data, and the calculation principles of the drift coefficient and the diffusion coefficient of other degradation processes are the same as the calculation principles of the drift coefficient and the diffusion coefficient of the mth degradation process.
In a specific embodiment, step S30 includes:
step S31: according to the formula (5), based onCurrent time t of the mth degradation processkCalculating a predicted value of the mth degradation process at a future time by using the corresponding sampling value, the transfer matrix, and the drift coefficient and the diffusion coefficient of the mth degradation process:
Figure BDA0003092884170000131
wherein the content of the first and second substances,
Figure BDA0003092884170000132
representing the current time t of the m-th degeneration processkDrift coefficient, phi, corresponding to the ship statekIndicates the current time tkThe corresponding state of the vessel is the state of the vessel,
Figure BDA0003092884170000133
representing the mth predicted time t of the degradation processk+1The corresponding predicted value is set to be a predicted value,
Figure BDA0003092884170000134
indicates the current time tkThe corresponding value of the sampled value is,
Figure BDA0003092884170000135
to satisfy
Figure BDA0003092884170000136
The random number of (2);
Figure BDA0003092884170000137
representing the mth predicted time t of the degradation processk+s-1Drift coefficient of corresponding ship state, from phik+s-1Is determined by the state of phik+s-1Representing future predicted time tk+s-1The corresponding ship state is determined by the state of the previous prediction time and the transition matrix,
Figure BDA0003092884170000138
representing the mth predicted time t of the degradation processk+sThe corresponding predicted value is set to be a predicted value,
Figure BDA0003092884170000139
representing future predicted time tk+s-1The corresponding predicted value, s is more than or equal to 2,
Figure BDA00030928841700001310
to satisfy
Figure BDA00030928841700001311
A distributed random number;
specifically, with the mth degeneration process, the current time is tkTo explain, the following steps are carried out: based on tkSampling values with known times
Figure BDA00030928841700001312
Known ship state phikAnd then according to a known phikObtained drift coefficient
Figure BDA00030928841700001313
It is also known that it is possible to use,
Figure BDA00030928841700001314
to satisfy
Figure BDA00030928841700001315
Is also known, (t) is the random number ofk+1-tk) Is preset, and thus can be obtained
Figure BDA00030928841700001316
I.e. the mth degradation process future predicted time tk+1And the corresponding predicted values are further obtained based on the principle, and the predicted values corresponding to a plurality of future predicted times are obtained.
Step S32: up to
Figure BDA00030928841700001317
Reaching the failure threshold of the mth degradation process for the first time, wherein the time interval between the moment reaching the failure threshold and the current moment is the failure time;
specifically, the failure threshold corresponding to each degradation process may be preset, and when a predicted value of a future time predicted by a certain degradation process reaches the failure threshold of the degradation process for the first time, a time interval between the predicted time reaching the failure threshold and the current time is the failure time. Further, the fact that the predicted value at a certain moment reaches the failure threshold of the degradation process for the first time means that: the predicted value at a certain moment is greater than or equal to the failure threshold value for the first time or the predicted value at a certain moment is less than or equal to the failure threshold value for the first time, and the specific situation depends on the change trend (increase or decrease) of data in the specific degradation process.
Step S33: and obtaining a plurality of failure times of the mth degradation process based on a Monte Carlo method, and further obtaining cumulative probability density distribution of the plurality of failure times of each degradation process.
In particular, taking the mth degeneration process as an example, a plurality of repeated tests (i.e. forming a plurality of test samples) are constructed according to the monte carlo method, for example, at the time t of passing the current momentkState of (1) solving future predicted time tk+1Predicted value of (2)
Figure BDA0003092884170000141
Then, n repeated tests are carried out in total, and n numbers meeting the requirement are randomly selected by combining the diffusion coefficient of the mth degradation process
Figure BDA0003092884170000142
Further obtain n
Figure BDA0003092884170000143
Further, by predicting the time tk+1State solution of (1) predicted time tk+2Predicted value of (2)
Figure BDA0003092884170000144
And repeating the test for n times according to the current time tkCorresponding ship state phikThe transfer matrix, the drift coefficient and the diffusion coefficient of the mth degradation process to obtain n groups
Figure BDA0003092884170000145
And
Figure BDA0003092884170000146
further obtain n
Figure BDA0003092884170000147
Based on the principle, the future time is predicted in sequence, if a certain sample reaches a failure threshold value at a certain future prediction time for the first time in midway, the failure time corresponding to the test sample is obtained, n failure times can be obtained based on the principle, and a plurality of failure times l of the mth degradation process are calculated based on the n failure times(m)Cumulative probability density distribution F(m)(l(m)) The specific number of n can be determined according to actual conditions.
The calculation process of the cumulative probability density distribution of the degradation process corresponding to the remaining dimension data in the M-dimensional measurement data in the historical period may refer to the calculation process of the cumulative probability density distribution of the mth degradation process, and details are not repeated here.
In a specific embodiment, step S40 includes:
obtaining the joint probability density distribution of the ship fault state according to the formula (6):
Figure BDA0003092884170000151
wherein F (l) represents that the current time is tkJoint probability density distribution of time, ship fault states, F(1)(l(1)) Indicating that the current time is tkCumulative probability density distribution of time to failure of the first degeneration process, F(2)(l(2)) Indicating that the current time is tkCumulative probability density distribution of time to failure of the second degeneration process, F(M)(l(M)) Indicating that the current time is tkCumulative probability density distribution of time to failure, phi, of the Mth degeneration processρA joint cumulative distribution function of M-dimensional normal distribution with mean as zero vector and covariance matrix as rho, phi-1An inverse cumulative distribution function representing a standard normal distribution.
In a particular embodiment, the method further includes calculating ρ according to equation (7):
Figure BDA0003092884170000152
wherein, Fi(l) Indicating that the current time is tiAnd then, i is more than or equal to 1 and less than or equal to k in the calculated joint probability density distribution of the ship fault state.
Specifically, the aforementioned F (l) is the current time tkJoint probability density distribution of vessel fault states for which a calculation is made, assuming tkThe measured data at the moment is unknown, i.e. the current moment is tk-1F can be obtained by the same method as described abovek-1(l)，
Figure BDA0003092884170000153
And likewise in turn give Fi(l)，
Figure BDA0003092884170000154
I is more than or equal to 1 and less than or equal to k; f obtained by the above calculationi(l) Substituting into formula (7), at this time, only one unknown parameter rho exists, and calculating according to formula (7) can obtain rho. Further, after ρ is obtained by calculation, ρ is substituted into equation (6), and the current time t is obtainedkPerforming estimated joint probability density distribution of the ship fault state; substituting rho into
Figure BDA0003092884170000155
The current time is tiAnd carrying out estimated joint probability density distribution of the ship fault state. And selecting a proper current moment according to actual requirements so as to predict the joint probability density distribution of the ship fault state.
The joint probability density distribution of the ship fault state can represent the probability of the ship fault before a certain prediction moment in the future, and further provides a reference basis for subsequent treatment such as whether the ship needs to be maintained or scrapped.
In a specific embodiment, the method further includes step S50: and calculating the ship life meeting the confidence rate based on the joint probability density distribution of the ship fault state and the confidence rate.
In a specific embodiment, step S50 includes:
step S51: respectively enabling a plurality of variables in the joint probability density distribution of the ship fault state to be equal to the ship life L meeting the confidence rate alpha, wherein the variables respectively represent a plurality of degradation process failure times;
step S52: according to equation (8), the ship life L is calculated:
F(l)＝F(L,L,...,L)＝1-α (8)
wherein F (l) represents that the current time is tkA joint probability density distribution of the ship's fault state.
Specifically, a plurality of variables in the joint probability density distribution of the ship fault state are respectively equal to the ship life L satisfying the confidence rate α, which can be expressed as: l(1)＝l(2)＝...＝l(M)L, where a plurality of variables L(1)To l(M)Respectively representing the failure time of the 1 st degradation process to the Mth degradation process; in combination with the set value of the confidence rate α and the formula (8), a specific value of L can be calculated, where L is the time that the ship can still work normally at the confidence rate α, i.e., the life of the ship at the confidence rate α. Optionally, the value of α may be 99% or 95%, and may be determined according to actual requirements.
An embodiment of the present application discloses a ship fault state prediction system, a schematic structural diagram of which is shown in fig. 2, and the system includes:
the data acquisition module is used for acquiring M-dimensional measurement data in a historical time period, wherein the M-dimensional measurement data comprises measurement data in a normal working state and measurement data in a maintenance state;
the transfer matrix calculation module is used for calculating to obtain a transfer matrix between a normal working state and a maintenance state based on the M-dimensional measurement data;
the drift coefficient and diffusion coefficient calculation module is used for calculating the drift coefficient and diffusion coefficient of each degradation process based on the M-dimensional measurement data, and the one-dimensional measurement data corresponds to one degradation process;
the cumulative probability density distribution acquisition module is used for setting a failure threshold value of each degradation process, and obtaining the cumulative probability density distribution of failure time of each degradation process based on the transfer matrix, the drift coefficient and the diffusion coefficient of each degradation process and in combination with a Monte Carlo method;
and the joint probability density distribution acquisition module is used for obtaining the joint probability density distribution of the ship fault state by combining the normal Copula function based on the cumulative probability density distribution of the failure time of each degradation process.
Compared with the prior art, the technical scheme is that a data acquisition module, a transfer matrix calculation module, a drift coefficient and diffusion coefficient calculation module, an accumulative probability density distribution acquisition module and a joint probability density distribution acquisition module are combined together, M-dimensional measurement data in a historical period is acquired, each dimension of the M-dimensional measurement data comprises normal working state measurement data and maintenance state measurement data, a transfer matrix between a normal working state and a maintenance state and a drift coefficient and a diffusion coefficient of each degradation process are calculated based on the acquired M-dimensional measurement data, the accumulative probability density distribution of failure time of each degradation process is obtained by combining a Monte Carlo method, and then a joint probability density distribution of a ship fault state is obtained by combining a normal copula function; the influence of maintenance activities on the ship fault state is introduced into the prediction on the ship fault state, and the accuracy of the ship fault state prediction is improved by considering the influence of the normal working state and the maintenance state on the ship fault state.
In a specific embodiment, the transition matrix calculation module includes:
a transfer rate calculation module for calculating the state transfer rate lambda of the normal working state to the maintenance state according to the following formula01And the transition rate lambda of the maintenance state to the normal operating state10：
Figure BDA0003092884170000181
Wherein n is01Representing the times of the normal working state to the maintenance state in the historical period; n is10Representing the times of the maintenance state to be converted into the normal working state in the historical time period; t is0Representing the total duration of the normal working state before the last state switching in the historical period; t is1Representing the total duration of the maintenance state before the last occurrence of the state switch in the history period;
a transition probability calculation module for calculating the state transition probability P of the normal working state to the maintenance state according to the following formula01And the probability P of state transition of maintenance state to normal working state10：
Figure BDA0003092884170000182
Wherein t represents the sampling period of the measurement data in the historical period;
a transition matrix generation module for converting the normal working state into a state transition probability P of the maintenance state01And the probability P of state transition of maintenance state to normal working state10Obtaining a transition matrix between the normal working state and the maintenance state
Figure BDA0003092884170000183
In a specific embodiment, the drift coefficient and diffusion coefficient calculation module includes:
a drift coefficient calculation module for calculating the drift coefficient of the m-th degradation process under normal working condition according to the following formula
Figure BDA0003092884170000184
And the drift coefficient in the mth degradation process maintenance state
Figure BDA0003092884170000185
Figure BDA0003092884170000186
Wherein phi isj0 denotes the normal operating state, phij1 represents the maintenance state, k represents the number of samples of the measurement data corresponding to the mth degradation process,
Figure BDA0003092884170000187
represents the j +1 th sample value of the m-th degeneration process,
Figure BDA0003092884170000188
j sample value, t, representing the m degradation processj+1Presentation collection
Figure BDA0003092884170000189
Time of (t)jPresentation collection
Figure BDA0003092884170000191
The time of day;
a diffusion coefficient calculation module for calculating the diffusion coefficient sigma of the mth degradation process according to the following formula(m)；
Figure BDA0003092884170000192
Wherein the content of the first and second substances,
Figure BDA0003092884170000193
to represent
Figure BDA0003092884170000194
The corresponding drift coefficient under the ship state is the drift coefficient under the normal working state
Figure BDA0003092884170000195
Or drift coefficient in maintenance state
Figure BDA0003092884170000196
In a specific embodiment, the system further comprises a life calculation module for calculating the life of the ship meeting the confidence rate based on the joint probability density distribution of the fault state of the ship and the confidence rate.
In a particular embodiment, the life calculation module includes:
the variable setting module is used for respectively enabling a plurality of variables in the joint probability density distribution of the ship fault state to be equal to the ship service life L meeting the confidence rate alpha;
the life calculating module is used for calculating the life L of the ship according to the following formula:
F(l)＝F(L,L,..,L)＝1-α
wherein F (l) represents that the current time is tkA joint probability density distribution of the ship's fault state.
The method embodiment and the system embodiment are realized based on the same principle, the related parts can be referenced mutually, and the same technical effect can be achieved.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (10)

1. A method for predicting a fault state of a ship, the method comprising:
collecting M-dimensional measurement data in a historical period, wherein the M-dimensional measurement data comprises measurement data in a normal working state and measurement data in a maintenance state;
calculating a transfer matrix between a normal working state and a maintenance state and a drift coefficient and a diffusion coefficient of each degradation process based on the M-dimensional measurement data, wherein the one-dimensional measurement data corresponds to one degradation process;
setting a failure threshold value of each degradation process, and obtaining cumulative probability density distribution of failure time of each degradation process by combining a Monte Carlo method based on the transfer matrix, the drift coefficient and the diffusion coefficient of each degradation process;
and obtaining the joint probability density distribution of the ship fault state by combining the normal Copula function based on the cumulative probability density distribution of the failure time of each degradation process.
2. The method of claim 1, wherein calculating a transition matrix between a normal operating state and a maintenance state based on the M-dimensional measurement data comprises:
obtaining the times of switching the normal working state to the maintenance state, the times of switching the maintenance state to the normal working state, the total duration of the normal working state before the last state switching in the history period and the total duration of the maintenance state based on the M-dimensional measurement data;
and calculating to obtain a transition matrix between the normal working state and the maintenance state by combining a maximum likelihood estimation method according to the times of converting the normal working state to the maintenance state, the times of converting the maintenance state to the normal working state, the total duration of the normal working state before the last state switching in the history period and the total duration of the maintenance state.
3. The method of claim 2, wherein computing a transition matrix between a normal operating state and a maintenance state comprises:
calculating the normal operating state transition according to the following formulaState transition rate λ of maintenance state01And the transition rate lambda of the maintenance state to the normal operating state10：
Figure FDA0003092884160000021
Wherein n is01Representing the times of the normal working state to the maintenance state in the historical period; n is10Representing the times of the maintenance state to be converted into the normal working state in the historical time period; t is0Representing the total duration of the normal working state before the last state switching in the historical period; t is1Representing the total duration of the maintenance state before the last occurrence of the state switch in the history period;
calculating the probability P of state transition from normal operation state to maintenance state according to the following formula01And the probability P of state transition of maintenance state to normal working state10：
Figure FDA0003092884160000022
Wherein t represents the sampling period of the measurement data in the historical period;
according to the state transition probability P of the normal working state to the maintenance state01And the probability P of state transition of maintenance state to normal working state10Obtaining a transition matrix between the normal working state and the maintenance state
Figure FDA0003092884160000023
4. The method of any one of claims 1 to 3, wherein calculating a drift coefficient and a diffusion coefficient for each degradation process based on the M-dimensional measurement data comprises:
calculating the drift coefficient of the m degradation process under the normal working state according to the following formula
Figure FDA0003092884160000024
And the drift coefficient in the mth degradation process maintenance state
Figure FDA0003092884160000025
Figure FDA0003092884160000026
Wherein phi isj0 denotes the normal operating state, phij1 represents the maintenance state, k represents the number of samples of the measurement data corresponding to the mth degradation process,
Figure FDA0003092884160000027
represents the j +1 th sample value of the m-th degeneration process,
Figure FDA0003092884160000028
j sample value, t, representing the m degradation processj+1Presentation collection
Figure FDA0003092884160000029
Time of (t)jPresentation collection
Figure FDA0003092884160000031
The time of day;
the diffusion coefficient σ of the mth degradation process is calculated according to the following formula(m)；
Figure FDA0003092884160000032
Wherein the content of the first and second substances,
Figure FDA0003092884160000033
to represent
Figure FDA0003092884160000034
The corresponding drift coefficient under the ship state is the drift coefficient under the normal working state
Figure FDA0003092884160000035
Or drift coefficient in maintenance state
Figure FDA0003092884160000036
5. The method of claim 1, wherein setting a failure threshold for each degradation process, and obtaining a cumulative probability density distribution of failure time for each degradation process based on the transition matrix, the drift coefficient and the diffusion coefficient for each degradation process in combination with a monte carlo method comprises:
based on the current time t of the m-th degeneration process according to the following formulakObtaining a predicted value of the mth degradation process at a future moment by using the corresponding sampling value, the transfer matrix, and the drift coefficient and the diffusion coefficient of the mth degradation process:
Figure FDA0003092884160000037
wherein the content of the first and second substances,
Figure FDA0003092884160000038
representing the current time t of the m-th degeneration processkDrift coefficient, phi, corresponding to the ship statekIndicates the current time tkThe corresponding state of the vessel is the state of the vessel,
Figure FDA0003092884160000039
representing the mth predicted time t of the degradation processk+1The corresponding predicted value is set to be a predicted value,
Figure FDA00030928841600000310
indicates the current time tkThe corresponding value of the sampled value is,
Figure FDA00030928841600000311
to satisfy
Figure FDA00030928841600000312
The random number of (2);
Figure FDA00030928841600000313
representing the mth predicted time t of the degradation processk+s-1Drift coefficient of corresponding ship state, from phik+s-1Is determined by the state of phik+s-1Representing future predicted time tk+s-1The corresponding ship state is determined by the state of the previous prediction time and the transition matrix,
Figure FDA00030928841600000314
representing the mth predicted time t of the degradation processk+sThe corresponding predicted value is set to be a predicted value,
Figure FDA00030928841600000315
representing future predicted time tk+s-1The corresponding predicted value, s is more than or equal to 2,
Figure FDA00030928841600000316
to satisfy
Figure FDA00030928841600000317
A distributed random number;
up to
Figure FDA0003092884160000041
Reaching the failure threshold of the mth degradation process for the first time, wherein the time interval between the moment reaching the failure threshold and the current moment is the failure time;
and obtaining a plurality of failure times of the mth degradation process based on a Monte Carlo method, and further obtaining cumulative probability density distribution of the plurality of failure times of each degradation process.
6. The method according to claim 1, wherein obtaining a joint probability density distribution of the ship fault state based on the cumulative probability density distribution of each degradation process failure time and a normal Copula function comprises:
obtaining a joint probability density distribution of the ship fault state according to the following formula:
F(l)＝C(F(1)(l(1)),F(2)(l(2)),...,F(M)(l(M))；ρ)
＝Φρ(Φ-1(F(1)(l(1)))，Φ-1(F(2)(l(2)))，...Φ-1(F(M)(l(M))))
wherein F (l) represents that the current time is tkJoint probability density distribution of time, ship fault states, F(1)(l(1)) Indicating that the current time is tkCumulative probability density distribution of time to failure of the first degeneration process, F(2)(l(2)) Indicating that the current time is tkCumulative probability density distribution of time to failure of the second degeneration process, F(M)(l(M)) Indicating that the current time is tkCumulative probability density distribution of time to failure, phi, of the Mth degeneration processρA joint cumulative distribution function of M-dimensional normal distribution with mean as zero vector and covariance matrix as rho, phi-1An inverse cumulative distribution function representing a standard normal distribution.
7. The method of claim 6, further comprising calculating p by the formula:
Figure FDA0003092884160000042
wherein, Fi(l) Indicating that the current time is tiUnion of calculated ship fault statesAnd (3) probability density distribution, i is more than or equal to 1 and less than or equal to k.
8. A ship fault condition prediction system, characterized in that the system comprises:
the data acquisition module is used for acquiring M-dimensional measurement data in a historical time period, wherein the M-dimensional measurement data comprises measurement data in a normal working state and measurement data in a maintenance state;
the transfer matrix calculation module is used for calculating to obtain a transfer matrix between a normal working state and a maintenance state based on the M-dimensional measurement data;
the drift coefficient and diffusion coefficient calculation module is used for calculating the drift coefficient and diffusion coefficient of each degradation process based on the M-dimensional measurement data, and the one-dimensional measurement data corresponds to one degradation process;
the cumulative probability density distribution acquisition module is used for setting a failure threshold value of each degradation process, and obtaining the cumulative probability density distribution of failure time of each degradation process based on the transfer matrix, the drift coefficient and the diffusion coefficient of each degradation process and in combination with a Monte Carlo method;
and the joint probability density distribution acquisition module is used for obtaining the joint probability density distribution of the ship fault state by combining the normal Copula function based on the cumulative probability density distribution of the failure time of each degradation process.
9. The system of claim 8, wherein the transition matrix calculation module comprises:
a transfer rate calculation module for calculating the state transfer rate lambda of the normal working state to the maintenance state according to the following formula01And the transition rate lambda of the maintenance state to the normal operating state10：
Figure FDA0003092884160000051
Wherein n is01Indicating the normal working state to be switched to maintenance state in historical periodThe number of states; n is10Representing the times of the maintenance state to be converted into the normal working state in the historical time period; t is0Representing the total duration of the normal working state before the last state switching in the historical period; t is1Representing the total duration of the maintenance state before the last occurrence of the state switch in the history period;
a transition probability calculation module for calculating the state transition probability P of the normal working state to the maintenance state according to the following formula01And the probability P of state transition of maintenance state to normal working state10：
Figure FDA0003092884160000061
Wherein t represents the sampling period of the measurement data in the historical period;
a transition matrix generation module for converting the normal working state into a state transition probability P of the maintenance state01And the probability P of state transition of maintenance state to normal working state10Obtaining a transition matrix between the normal working state and the maintenance state
Figure FDA0003092884160000062
10. The system of claim 8 or 9, wherein the drift coefficient and diffusion coefficient calculation module comprises:
a drift coefficient calculation module for calculating the drift coefficient of the m-th degradation process under normal working condition according to the following formula
Figure FDA0003092884160000063
And the drift coefficient in the mth degradation process maintenance state
Figure FDA0003092884160000064
Figure FDA0003092884160000065
Wherein phi isj0 denotes the normal operating state, phij1 represents the maintenance state, k represents the number of samples of the measurement data corresponding to the mth degradation process,
Figure FDA0003092884160000066
represents the j +1 th sample value of the m-th degeneration process,
Figure FDA0003092884160000067
j sample value, t, representing the m degradation processj+1Presentation collection
Figure FDA0003092884160000068
Time of (t)jPresentation collection
Figure FDA0003092884160000069
The time of day;
a diffusion coefficient calculation module for calculating the diffusion coefficient sigma of the mth degradation process according to the following formula(m)；
Figure FDA00030928841600000610
Wherein the content of the first and second substances,
Figure FDA00030928841600000611
to represent
Figure FDA00030928841600000612
The corresponding drift coefficient under the ship state is the drift coefficient under the normal working state
Figure FDA00030928841600000613
Or drift coefficient in maintenance state
Figure FDA00030928841600000614
CN202110601739.8A 2021-05-31 2021-05-31 Ship fault state prediction method and system Pending CN113516299A (en)

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